/**
 * Copyright (c) 2011, The University of Southampton and the individual contributors.
 * All rights reserved.
 *
 * Redistribution and use in source and binary forms, with or without modification,
 * are permitted provided that the following conditions are met:
 *
 *   *  Redistributions of source code must retain the above copyright notice,
 *      this list of conditions and the following disclaimer.
 *
 *   *  Redistributions in binary form must reproduce the above copyright notice,
 *      this list of conditions and the following disclaimer in the documentation
 *      and/or other materials provided with the distribution.
 *
 *   *  Neither the name of the University of Southampton nor the names of its
 *      contributors may be used to endorse or promote products derived from this
 *      software without specific prior written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
 * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
 * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
 * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
 * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
 * ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
 * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 */
package org.openimaj.feature.local.matcher;

import java.util.ArrayList;
import java.util.List;

import org.openimaj.citation.annotation.Reference;
import org.openimaj.citation.annotation.ReferenceType;
import org.openimaj.citation.annotation.References;
import org.openimaj.feature.DoubleFVComparison;
import org.openimaj.feature.local.LocalFeature;
import org.openimaj.util.pair.Pair;

/**
 * Basic local feature matcher. Matches interest points by finding closest two
 * interest points to target and checking whether the distance between the two
 * matches is sufficiently large.
 * 
 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
 * @param <T>
 */
@References(references = {
                @Reference(
                                type = ReferenceType.Article,
                                author = { "David Lowe" },
                                title = "Distinctive image features from scale-invariant keypoints",
                                year = "2004",
                                journal = "IJCV",
                                pages = { "91", "110" },
                                month = "January",
                                number = "2",
                                volume = "60"),
                @Reference(
                                type = ReferenceType.Inproceedings,
                                author = { "David Lowe" },
                                title = "Object recognition from local scale-invariant features",
                                year = "1999",
                                booktitle = "Proc. of the International Conference on Computer Vision {ICCV}",
                                pages = { "1150", "1157" }
                )
})
public class BasicMatcher<T extends LocalFeature<?, ?>> implements LocalFeatureMatcher<T> {
        protected List<T> modelKeypoints;
        protected List<Pair<T>> matches;
        protected int thresh = 8;

        /**
         * Initialise the matcher setting the threshold which the difference between
         * the scores of the top two best matches must differ in order to count the
         * first as a good match.
         * 
         * @param threshold
         */
        public BasicMatcher(int threshold)
        {
                matches = new ArrayList<Pair<T>>();
                thresh = threshold;
        }

        /**
         * @return List of pairs of matching keypoints
         */
        @Override
        public List<Pair<T>> getMatches() {
                return matches;
        }

        @Override
        public boolean findMatches(List<T> keys1)
        {
                matches = new ArrayList<Pair<T>>();

                /*
                 * Match the keys in list keys1 to their best matches in keys2.
                 */
                for (final T k : keys1) {
                        final T match = checkForMatch(k, modelKeypoints);

                        if (match != null) {
                                matches.add(new Pair<T>(k, match));
                        }
                }

                return true;
        }

        /**
         * This searches through the keypoints in klist for the two closest matches
         * to key. If the closest is less than <code>threshold</code> times distance
         * to second closest, then return the closest match. Otherwise, return NULL.
         */
        protected T checkForMatch(T query, List<T> features)
        {
                double distsq1 = Double.MAX_VALUE, distsq2 = Double.MAX_VALUE;
                T minkey = null;

                // find two closest matches
                for (final T target : features) {
                        final double dsq = target.getFeatureVector().asDoubleFV()
                                        .compare(query.getFeatureVector().asDoubleFV(), DoubleFVComparison.EUCLIDEAN);

                        if (dsq < distsq1) {
                                distsq2 = distsq1;
                                distsq1 = dsq;
                                minkey = target;
                        } else if (dsq < distsq2) {
                                distsq2 = dsq;
                        }
                }

                // check the distance against the threshold
                if (10 * 10 * distsq1 < thresh * thresh * distsq2) {
                        return minkey;
                }
                else
                        return null;
        }

        @Override
        public void setModelFeatures(List<T> modelkeys) {
                modelKeypoints = modelkeys;
        }

        /**
         * Set the matching threshold
         * 
         * @param thresh
         *            the threshold
         */
        public void setThreshold(int thresh) {
                this.thresh = thresh;
        }
}